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AR-Net: Adaptive Frame Resolution for Efficient Action Recognition

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12352))

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Abstract

Action recognition is an open and challenging problem in computer vision. While current state-of-the-art models offer excellent recognition results, their computational expense limits their impact for many real-world applications. In this paper, we propose a novel approach, called AR-Net (Adaptive Resolution Network), that selects on-the-fly the optimal resolution for each frame conditioned on the input for efficient action recognition in long untrimmed videos. Specifically, given a video frame, a policy network is used to decide what input resolution should be used for processing by the action recognition model, with the goal of improving both accuracy and efficiency. We efficiently train the policy network jointly with the recognition model using standard back-propagation. Extensive experiments on several challenging action recognition benchmark datasets well demonstrate the efficacy of our proposed approach over state-of-the-art methods. The project page can be found at https://mengyuest.github.io/AR-Net.

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Notes

  1. 1.

    The notation here is for brevity. Actually, the output for \(\varPhi \) is a feature vector, whereas the output for \(\varPsi _{L-1}\) is a prediction. In implementation, we use a fully connected layer after the feature vector to get the prediction.

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Acknowledgement

This work is supported by IARPA via DOI/IBC contract number D17PC00341. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. This work is partly supported by the MIT-IBM Watson AI Lab.

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Authors and Affiliations

  1. MIT-IBM Watson AI Lab, IBM Research, New York, USA

    Yue Meng, Chung-Ching Lin, Rameswar Panda, Prasanna Sattigeri, Leonid Karlinsky, Aude Oliva, Kate Saenko & Rogerio Feris

  2. Boston University, Boston, USA

    Kate Saenko

  3. Massachusetts Institute of Technology, Cambridge, USA

    Aude Oliva

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  1. Yue Meng
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  2. Chung-Ching Lin
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Correspondence to Yue Meng .

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  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Meng, Y. et al. (2020). AR-Net: Adaptive Frame Resolution for Efficient Action Recognition. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12352. Springer, Cham. https://doi.org/10.1007/978-3-030-58571-6_6

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